Knowing Thy Neighbor: What Information Neighbors Have and How Best to Elicit it Reshmaan Hussam, Natalia Rigol, Benjamin N. Roth June 2, 2016 Hussam, Rigol and Roth June 2, 2016 1 / 22
Introduction Outline Introduction 1 The Experiment 2 Results 3 Conclusion 4 Hussam, Rigol and Roth June 2, 2016 2 / 22
Introduction Motivation 1 Asymmetric information is a major obstacle to providing services and support for the poor. Hussam, Rigol and Roth June 2, 2016 3 / 22
Introduction Motivation 1 Asymmetric information is a major obstacle to providing services and support for the poor. Ideally would like to allocate capital to entrepreneurs with high marginal returns, credit to reliable borrowers, subsidies to the poor, etc. Hussam, Rigol and Roth June 2, 2016 3 / 22
Introduction Motivation 1 Asymmetric information is a major obstacle to providing services and support for the poor. Ideally would like to allocate capital to entrepreneurs with high marginal returns, credit to reliable borrowers, subsidies to the poor, etc. 2 However, formal information about the intended targets of these services is sparse or non existent. Hussam, Rigol and Roth June 2, 2016 3 / 22
Introduction Motivation 1 Asymmetric information is a major obstacle to providing services and support for the poor. Ideally would like to allocate capital to entrepreneurs with high marginal returns, credit to reliable borrowers, subsidies to the poor, etc. 2 However, formal information about the intended targets of these services is sparse or non existent. 3 Governments, NGOs, and MFIs screening recipients for aid and credit may rely on community information. But... Hussam, Rigol and Roth June 2, 2016 3 / 22
Introduction Motivation 1 Asymmetric information is a major obstacle to providing services and support for the poor. Ideally would like to allocate capital to entrepreneurs with high marginal returns, credit to reliable borrowers, subsidies to the poor, etc. 2 However, formal information about the intended targets of these services is sparse or non existent. 3 Governments, NGOs, and MFIs screening recipients for aid and credit may rely on community information. But... What is the quality of the information held by community members? Hussam, Rigol and Roth June 2, 2016 3 / 22
Introduction Motivation 1 Asymmetric information is a major obstacle to providing services and support for the poor. Ideally would like to allocate capital to entrepreneurs with high marginal returns, credit to reliable borrowers, subsidies to the poor, etc. 2 However, formal information about the intended targets of these services is sparse or non existent. 3 Governments, NGOs, and MFIs screening recipients for aid and credit may rely on community information. But... What is the quality of the information held by community members? Should we be taking incenetives seriously? Hussam, Rigol and Roth June 2, 2016 3 / 22
Introduction Highlights 1 Members of peri-urban communities have high quality information about one another regarding, among other things, their entrepreneurial ability. Hussam, Rigol and Roth June 2, 2016 4 / 22
Introduction Highlights 1 Members of peri-urban communities have high quality information about one another regarding, among other things, their entrepreneurial ability. 2 Community members distort their reports when they’re being used to inform real allocations (the distribution of grants). Hussam, Rigol and Roth June 2, 2016 4 / 22
Introduction Highlights 1 Members of peri-urban communities have high quality information about one another regarding, among other things, their entrepreneurial ability. 2 Community members distort their reports when they’re being used to inform real allocations (the distribution of grants). 3 Simple techniques motivated by mechanism design are effective in realigning incentives for truthfulness. Hussam, Rigol and Roth June 2, 2016 4 / 22
Introduction Highlights 1 Members of peri-urban communities have high quality information about one another regarding, among other things, their entrepreneurial ability. 2 Community members distort their reports when they’re being used to inform real allocations (the distribution of grants). 3 Simple techniques motivated by mechanism design are effective in realigning incentives for truthfulness. Monetary incentives Hussam, Rigol and Roth June 2, 2016 4 / 22
Introduction Highlights 1 Members of peri-urban communities have high quality information about one another regarding, among other things, their entrepreneurial ability. 2 Community members distort their reports when they’re being used to inform real allocations (the distribution of grants). 3 Simple techniques motivated by mechanism design are effective in realigning incentives for truthfulness. Monetary incentives Privacy Hussam, Rigol and Roth June 2, 2016 4 / 22
Introduction Highlights 1 Members of peri-urban communities have high quality information about one another regarding, among other things, their entrepreneurial ability. 2 Community members distort their reports when they’re being used to inform real allocations (the distribution of grants). 3 Simple techniques motivated by mechanism design are effective in realigning incentives for truthfulness. Monetary incentives Privacy Cross reporting Hussam, Rigol and Roth June 2, 2016 4 / 22
Introduction Highlights 1 Members of peri-urban communities have high quality information about one another regarding, among other things, their entrepreneurial ability. 2 Community members distort their reports when they’re being used to inform real allocations (the distribution of grants). 3 Simple techniques motivated by mechanism design are effective in realigning incentives for truthfulness. Monetary incentives Privacy Cross reporting Zero sum elicitation Hussam, Rigol and Roth June 2, 2016 4 / 22
The Experiment Outline Introduction 1 The Experiment 2 Results 3 Conclusion 4 Hussam, Rigol and Roth June 2, 2016 5 / 22
The Experiment The Sample Conducted census of all business owners in 9 peri-urban communities around Amravati, India Hussam, Rigol and Roth June 2, 2016 6 / 22
The Experiment The Sample Conducted census of all business owners in 9 peri-urban communities around Amravati, India 1576 households had a non-farm business with capital < $1000 and no paid, permenant employees Hussam, Rigol and Roth June 2, 2016 6 / 22
The Experiment The Sample Conducted census of all business owners in 9 peri-urban communities around Amravati, India 1576 households had a non-farm business with capital < $1000 and no paid, permenant employees 1380 participated in our study Hussam, Rigol and Roth June 2, 2016 6 / 22
The Experiment Timeline Census: September 2015 Recruitment: October 2015 Baseline survey: December 2015 - April 2016 Elicitation exercise: February 2016 - May 2016 Followup: Ongoing Hussam, Rigol and Roth June 2, 2016 7 / 22
The Experiment Elicitation Questions We ask questions about Marginal Return to Capital Income Profits Assets Medical Expenses Work Hours Digit Span Likelihood to Repay a Loan Business Ability Deservingness of the Grant Hussam, Rigol and Roth June 2, 2016 8 / 22
The Experiment Elicitation Exercise We asked respondents answers to above questions during baseline (before they had knowledge of the elicitation exercise) We then clubbed the entrepreneurs into groups of 4-6 based on geographic proximity and invited them to a central hall to rank one another along above dimensions. Groups and individuals randomized into the following treatments Hussam, Rigol and Roth June 2, 2016 9 / 22
The Experiment Experimental Design Hussam, Rigol and Roth June 2, 2016 10 / 22
The Experiment Non-Random Design Features Relative Rankings and Quintiles Hussam, Rigol and Roth June 2, 2016 11 / 22
The Experiment Non-Random Design Features Relative Rankings and Quintiles “Cross reporting” Hussam, Rigol and Roth June 2, 2016 11 / 22
The Experiment Non-Random Design Features Relative Rankings and Quintiles “Cross reporting” Peer Prediction... Hussam, Rigol and Roth June 2, 2016 11 / 22
The Experiment Elicitation in the Field Hussam, Rigol and Roth June 2, 2016 12 / 22
The Experiment Elicitation in the Field Hussam, Rigol and Roth June 2, 2016 13 / 22
Results Outline Introduction 1 The Experiment 2 Results 3 Conclusion 4 Hussam, Rigol and Roth June 2, 2016 14 / 22
Results Main Specifications How much people know: Outcome i = α 0 + α 1 Rank ik + X i + γ c + ǫ ik for person i , cluster c , ranker k . Hussam, Rigol and Roth June 2, 2016 15 / 22
Results Main Specifications How much people know: Outcome i = α 0 + α 1 Rank ik + X i + γ c + ǫ ik for person i , cluster c , ranker k . The effect of our treatments: Outcome i = α 0 + α 1 Rank ik + � n β n Rank ik × Treatment n + X i + γ c + � n δ n Treatment n + ǫ ik Hussam, Rigol and Roth June 2, 2016 15 / 22
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